Beyond transparency: computational reliabilism as an externalist epistemology of algorithms
- URL: http://arxiv.org/abs/2502.20402v1
- Date: Mon, 27 Jan 2025 14:31:47 GMT
- Title: Beyond transparency: computational reliabilism as an externalist epistemology of algorithms
- Authors: Juan Manuel DurĂ¡n,
- Abstract summary: Current approaches to justification the transparency of algorithms involve elucidating their internal mechanisms.<n>In contrast, advocate for an externalist algorithm that I term reliam (CR)<n>CR posits that an algorithm's output is justified if it is produced by a reliable algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This chapter is interested in the epistemology of algorithms. As I intend to approach the topic, this is an issue about epistemic justification. Current approaches to justification emphasize the transparency of algorithms, which entails elucidating their internal mechanisms -- such as functions and variables -- and demonstrating how (or that) these produce outputs. Thus, the mode of justification through transparency is contingent on what can be shown about the algorithm and, in this sense, is internal to the algorithm. In contrast, I advocate for an externalist epistemology of algorithms that I term computational reliabilism (CR). While I have previously introduced and examined CR in the field of computer simulations ([42, 53, 4]), this chapter extends this reliabilist epistemology to encompass a broader spectrum of algorithms utilized in various scientific disciplines, with a particular emphasis on machine learning applications. At its core, CR posits that an algorithm's output is justified if it is produced by a reliable algorithm. A reliable algorithm is one that has been specified, coded, used, and maintained utilizing reliability indicators. These reliability indicators stem from formal methods, algorithmic metrics, expert competencies, cultures of research, and other scientific endeavors. The primary aim of this chapter is to delineate the foundations of CR, explicate its operational mechanisms, and outline its potential as an externalist epistemology of algorithms.
Related papers
- Position: We Need An Algorithmic Understanding of Generative AI [7.425924654036041]
This position paper proposes AlgEval: a framework for systematic research into the algorithms that LLMs learn and use.<n>AlgEval aims to uncover algorithmic primitives, reflected in latent representations, attention, and inference-time compute, and their algorithmic composition to solve task-specific problems.
arXiv Detail & Related papers (2025-07-10T08:38:47Z) - From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models [63.188607839223046]
This survey focuses on the benefits of scaling compute during inference.
We explore three areas under a unified mathematical formalism: token-level generation algorithms, meta-generation algorithms, and efficient generation.
arXiv Detail & Related papers (2024-06-24T17:45:59Z) - Mathematical Algorithm Design for Deep Learning under Societal and
Judicial Constraints: The Algorithmic Transparency Requirement [65.26723285209853]
We derive a framework to analyze whether a transparent implementation in a computing model is feasible.
Based on previous results, we find that Blum-Shub-Smale Machines have the potential to establish trustworthy solvers for inverse problems.
arXiv Detail & Related papers (2024-01-18T15:32:38Z) - Measuring, Interpreting, and Improving Fairness of Algorithms using
Causal Inference and Randomized Experiments [8.62694928567939]
We present an algorithm-agnostic framework (MIIF) to Measure, Interpret, and Improve the Fairness of an algorithmic decision.
We measure the algorithm bias using randomized experiments, which enables the simultaneous measurement of disparate treatment, disparate impact, and economic value.
We also develop an explainable machine learning model which accurately interprets and distills the beliefs of a blackbox algorithm.
arXiv Detail & Related papers (2023-09-04T19:45:18Z) - Comprehensive Algorithm Portfolio Evaluation using Item Response Theory [0.19116784879310023]
IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset.
We present a modified IRT-based framework for evaluating a portfolio of algorithms across a repository of datasets.
arXiv Detail & Related papers (2023-07-29T00:48:29Z) - A Gold Standard Dataset for the Reviewer Assignment Problem [117.59690218507565]
"Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper.
Our dataset consists of 477 self-reported expertise scores provided by 58 researchers.
For the task of ordering two papers in terms of their relevance for a reviewer, the error rates range from 12%-30% in easy cases to 36%-43% in hard cases.
arXiv Detail & Related papers (2023-03-23T16:15:03Z) - Instance-Dependent Confidence and Early Stopping for Reinforcement
Learning [99.57168572237421]
Various algorithms for reinforcement learning (RL) exhibit dramatic variation in their convergence rates as a function of problem structure.
This research provides guarantees that explain textitex post the performance differences observed.
A natural next step is to convert these theoretical guarantees into guidelines that are useful in practice.
arXiv Detail & Related papers (2022-01-21T04:25:35Z) - Using the Full-text Content of Academic Articles to Identify and
Evaluate Algorithm Entities in the Domain of Natural Language Processing [7.163189900803623]
This article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field.
A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching.
The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm.
arXiv Detail & Related papers (2020-10-21T08:24:18Z) - A black-box adversarial attack for poisoning clustering [78.19784577498031]
We propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms.
We show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
arXiv Detail & Related papers (2020-09-09T18:19:31Z) - Discovering Reinforcement Learning Algorithms [53.72358280495428]
Reinforcement learning algorithms update an agent's parameters according to one of several possible rules.
This paper introduces a new meta-learning approach that discovers an entire update rule.
It includes both 'what to predict' (e.g. value functions) and 'how to learn from it' by interacting with a set of environments.
arXiv Detail & Related papers (2020-07-17T07:38:39Z) - Active Model Estimation in Markov Decision Processes [108.46146218973189]
We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP)
We show that our Markov-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime.
arXiv Detail & Related papers (2020-03-06T16:17:24Z) - Algorithmic Fairness [11.650381752104298]
It is crucial to develop AI algorithms that are not only accurate but also objective and fair.
Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness.
arXiv Detail & Related papers (2020-01-21T19:01:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.